import tensorflow as tf
from numpy.random import RandomState

batch_size = 8

w1 = tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))
w2 = tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))

x = tf.placeholder(tf.float32,shape=(None,2),name="x-input")
y_ = tf.placeholder(tf.float32,shape=(None,1),name="y-input")

a = tf.matmul(x,w1)
y = tf.matmul(a,w2)

learning_rate = 0.001
cross_entropy = -tf.reduce_mean(y_*tf.log(tf.clip_by_value(y,1e-10,1.0)))
train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)

# initial simulation dataset
rdm = RandomState(1)
dataset_size = 128
X = rdm.rand(dataset_size,2)

Y = [[int(x1+x2<1)] for(x1,x2) in X]

with tf.Session() as sess:
    init_op = tf.initialize_all_variables()
    sess.run(init_op)
    print(sess.run(w1))
    print(sess.run(w2))

    STEPS = 5000
    for i in range(STEPS):
        start = (i*batch_size)%dataset_size
        end = min(start+batch_size,dataset_size)

        sess.run(train_step,
                 feed_dict={x:X[start:end],y_:Y[start:end]})
        if i % 1000 == 0:
            total_cross_entropy = sess.run(cross_entropy,feed_dict={x:X,y_:Y})
            print("After %d training steps, cross entropy on all data is %g"%(i,total_cross_entropy))

    print(sess.run(w1))
    print(sess.run(w2))

